Personalized Visited-POI Assignment to Individual Raw GPS Trajectories

• Published in: ACM transactions on spatial algorithms and systems 2019-09, Vol.5 (3), p.1-28
• Main Authors: Suzuki, Jun, Suhara, Yoshihiko, Toda, Hiroyuki, Nishida, Kyosuke
• Format: Article
• Language: English
• ISSN: 2374-0353; 2374-0361
• DOI: 10.1145/3317667

Knowledge discovery from GPS trajectory data is an essential topic in several scientific areas, including data mining, human behavior analysis, and user modeling. This article proposes a task that assigns personalized visited points of interest (POIs). Its goal is to assign every fine-grain location (i.e., POIs) that a user actually visited, which we call visited-POI , to the corresponding span of his or her (personal) GPS trajectories. We also introduce a novel algorithm to solve this assignment task. First, we exhaustively extract stay-points as span candidates of visits using a variant of a conventional stay-point extraction method and then extract POIs that are located close to the extracted stay-points as visited-POI candidates. Then, we simultaneously predict which stay-points and POIs can be actual user visits by considering various aspects, which we formulate as integer linear programming. Experimental results conducted on a real user dataset show that our method achieves higher accuracy in the visited-POI assignment task than the various cascaded procedures of conventional methods.